Understanding what's hard in learning about complex systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One approach to conceptual change suggests that ontological barriers may impose beliefs that contribute to learners' misconceptions and misunderstanding of many science concepts (e.g., Chi, Slotta, and deLeeuw, 1994). If beliefs about the nature of the world affect how one explains observations and the functioning of phenomena then it is possible that the lack of certain types of explanations may impose substantial limitations to learning. Overcoming problems in learning concepts such as diffusion, force, evolution, may require instruction of an ontological category (i.e., emergent causal processes), which is unfamiliar to most novice learners. We argue that it may be possible to accomplish this objective using complex systems thinking. This study investigated the acquisition of a set of complex systems concepts through simulations in an attempt to identify which concepts are easier and which are more difficult to learn and apply as an alternative causal explanation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it